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554e002747
This is an implementation of so-called downhill simplex method (https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method) Please, let me know if you have any comments, whoever you'd be.
64 lines
2.3 KiB
C++
64 lines
2.3 KiB
C++
#include "test_precomp.hpp"
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#include <cstdlib>
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#include <cmath>
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#include <algorithm>
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static void mytest(cv::Ptr<cv::optim::DownhillSolver> solver,cv::Ptr<cv::optim::Solver::Function> ptr_F,cv::Mat& x,cv::Mat& step,
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cv::Mat& etalon_x,double etalon_res){
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solver->setFunction(ptr_F);
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int ndim=MAX(step.cols,step.rows);
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solver->setInitStep(step);
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cv::Mat settedStep;
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solver->getInitStep(settedStep);
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ASSERT_TRUE(settedStep.rows==1 && settedStep.cols==ndim);
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ASSERT_TRUE(std::equal(step.begin<double>(),step.end<double>(),settedStep.begin<double>()));
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std::cout<<"step setted:\n\t"<<step<<std::endl;
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double res=solver->minimize(x);
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std::cout<<"res:\n\t"<<res<<std::endl;
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std::cout<<"x:\n\t"<<x<<std::endl;
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std::cout<<"etalon_res:\n\t"<<etalon_res<<std::endl;
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std::cout<<"etalon_x:\n\t"<<etalon_x<<std::endl;
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double tol=solver->getTermCriteria().epsilon;
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ASSERT_TRUE(std::abs(res-etalon_res)<tol);
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/*for(cv::Mat_<double>::iterator it1=x.begin<double>(),it2=etalon_x.begin<double>();it1!=x.end<double>();it1++,it2++){
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ASSERT_TRUE(std::abs((*it1)-(*it2))<tol);
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}*/
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std::cout<<"--------------------------\n";
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}
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class SphereF:public cv::optim::Solver::Function{
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public:
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double calc(const double* x)const{
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return x[0]*x[0]+x[1]*x[1];
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}
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};
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class RosenbrockF:public cv::optim::Solver::Function{
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double calc(const double* x)const{
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return 100*(x[1]-x[0]*x[0])*(x[1]-x[0]*x[0])+(1-x[0])*(1-x[0]);
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}
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};
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TEST(Optim_Downhill, regression_basic){
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cv::Ptr<cv::optim::DownhillSolver> solver=cv::optim::createDownhillSolver();
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#if 1
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{
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cv::Ptr<cv::optim::Solver::Function> ptr_F(new SphereF());
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cv::Mat x=(cv::Mat_<double>(1,2)<<1.0,1.0),
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step=(cv::Mat_<double>(2,1)<<-0.5,-0.5),
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etalon_x=(cv::Mat_<double>(1,2)<<-0.0,0.0);
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double etalon_res=0.0;
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mytest(solver,ptr_F,x,step,etalon_x,etalon_res);
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}
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#endif
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#if 1
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{
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cv::Ptr<cv::optim::Solver::Function> ptr_F(new RosenbrockF());
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cv::Mat x=(cv::Mat_<double>(2,1)<<0.0,0.0),
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step=(cv::Mat_<double>(2,1)<<0.5,+0.5),
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etalon_x=(cv::Mat_<double>(2,1)<<1.0,1.0);
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double etalon_res=0.0;
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mytest(solver,ptr_F,x,step,etalon_x,etalon_res);
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}
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#endif
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}
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